Publication: A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
dc.contributor.author | Urdangarin Iztueta, Arantxa | |
dc.contributor.author | Goicoa Mangado, Tomás | |
dc.contributor.author | Kneib, Thomas | |
dc.contributor.author | Ugarte Martínez, María Dolores | |
dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
dc.date.accessioned | 2024-05-24T11:10:31Z | |
dc.date.available | 2024-05-24T11:10:31Z | |
dc.date.issued | 2024 | |
dc.date.updated | 2024-05-24T11:03:09Z | |
dc.description.abstract | Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses. | en |
dc.description.sponsorship | This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Urdangarin, A., Goicoa, T., Kneib, T., Ugarte, M. D. (2024) A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models. Spatial Statistics, 59, 1-18. https://doi.org/10.1016/j.spasta.2023.100804. | es_ES |
dc.identifier.doi | 10.1016/j.spasta.2023.100804 | |
dc.identifier.issn | 2211-6753 | |
dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/48189 | |
dc.language.iso | eng | en |
dc.publisher | Elsevier | en |
dc.relation.ispartof | Spatial Statistics (2024), vol. 59, 100804 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.1016/j.spasta.2023.100804 | |
dc.rights | © 2024 The Authors. This is an open access article under the CC BY-NC-ND license. | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Crimes against women | en |
dc.subject | M-models | en |
dc.subject | Spatial confounding | en |
dc.subject | Spatial+ | en |
dc.title | A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models | en |
dc.type | Artículo / Artikulua | es |
dc.type | info:eu-repo/semantics/article | en |
dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 73ddf3f0-4c5d-426b-b308-e89dbbb1c884 | |
relation.isAuthorOfPublication | 77ba75f3-a30d-4f01-8cc5-9de6d3a10d8d | |
relation.isAuthorOfPublication | e87ff19e-9d36-4286-989b-cafd391dff9d | |
relation.isAuthorOfPublication.latestForDiscovery | 73ddf3f0-4c5d-426b-b308-e89dbbb1c884 |
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